Automated task planning for robots faces great challenges in that the sequences of events needed for a particular task are mostly required to be hard-coded. This can be a cumbersome process, especially, when the user wants a robot to learn a large number of similar tasks with different objects that are semantically related. We propose a novel approach of user preference-based integrated multi-memory model (pMM-ART). This approach focuses on exploiting a semantic hierarchy of objects alongside an episodic memory for enhancing the behavior of an autonomous agent. We analyze the functioning principle of the proposed model by teaching it a few distinct domestic tasks and observe that it is able to carry out a large number of similar tasks based on the semantic similarities between learned objects. We also demonstrate, via experiments using Mybot, our ability to reach those goals that are not possible without the integration of semantic knowledge with episodic memory.
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This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea (MSIP) (No. NRF-2014R1A2A1A10051551) and the Technology Innovation Program, 10045252, funded by the Korea MOTIE. The authors would like to thank Yong-Ho Yoo for his guidance during experiments on Mybot. The authors would also like to thank Jennifer Olsen, a post-doc at Computer–Human Interaction in Learning and Instruction Lab., for her feedback on the draft.
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Nasir, J., Kim, D. & Kim, J. ART neural network-based integration of episodic memory and semantic memory for task planning for robots. Auton Robot 43, 2163–2182 (2019). https://doi.org/10.1007/s10514-019-09868-x
- Adaptive resonance theory
- Task planning
- Semantic memory
- Episodic memory
- User preference